As federated learning is essential for institutions dealing with privacy such as medical, driving, voice, and facial data, it is difficult for each institution to share data.
Though training data and models are publicly available with open-source machine learning libraries in Tensorflow or Keras, it is not easy to share training models in federated learning.
While each FL client has different amounts of data, computing power, and learning schedule, the client performs the learning process in federated learning asynchronously [1]}.
In addition, there is no convenient tool for developers to exchange federated learning machine learning models.
